NIMG-59. EVALUATION OF THE RESPONSE ASSESSMENT CRITERIA IN NEWLY DIAGNOSED AND RECURRENT GLIOBLASTOMA
Bibliographic record
Abstract
Abstract BACKGROUND We sought to compare the Response Assessment in Neuro-Oncology (RANO), modified RANO (mRANO), and immunotherapy RANO (iRANO) in a large population of patients with newly diagnosed (nGBM) and recurrent (rGBM) glioblastoma. METHODS Bidimensional measurements of enhancing disease and FLAIR sequence evaluation were performed by two independent readers on brain MRIs of consecutive patients with IDH-wildtype nGBM and rGBM treated at a single institution. Discrepancies were evaluated by a third reader. Dates of disease progression (PD) were identified using RANO, mRANO, iRANO, and other response assessment criteria variations. Spearman’s correlations between PFS and OS were calculated using iterative multiple imputations for censored observations. RESULTS 526 nGBM and 580 rGBM cases were included. Spearman’s correlations were not significantly different between RANO and mRANO in nGBM (0.69 [95% CI 0.62 to 0.75] vs. 0.67 [0.60, 0.73]) and rGBM (0.48 [0.40, 0.55] vs. 0.50 [0.42, 0.57]). Evaluation of FLAIR did not improve the correlation in patients who received antiangiogenic therapy. Acquisition of confirmation scans was associated with increased correlation only when PD was identified within 12 weeks of completion of radiation in nGBM. The use of the post-radiation MRI as a baseline was associated with increased correlation compared to use of the pre-radiation MRI in nGBM (0.67 [0.60, 0.73] vs. 0.53 [0.42, 0.62]). The correlation with iRANO was similar to RANO and mRANO among 98 patients with nGBM and 175 patients with rGBM who received immunotherapy. CONCLUSIONS RANO and mRANO demonstrated similar correlations between PFS and OS. The evaluation of FLAIR can be omitted, while confirmation scans were only beneficial in nGBM in the first 12 weeks after completion of radiation. There was a trend in favor of the post-radiation MRI as the baseline scan in nGBM. The use of iRANO criteria did not add a significant benefit in patients who received immunotherapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".